Predictions should never be 100% confident.

In short, statistical inference follows these steps: Ask a meaningful question. Formulate a hypothesis test, setting the status quo as the null hypothesis, and what you hope to be true as the alternative hypothesis. Establish a significance level. (5% or 0.05 is an arbitrary but often-used number.) Calculate a p-value based on a statistical test.

What is a false positive error? It's when evidence appears to confirm the reality of the alternate hypothesis when instead it should have been rejected (e.g., a man has a positive pregnancy test).

Observational data should not be used (at least, not exclusively) to derive causal relationships.

unsupervised learning. You didn't come in with preconceived notions about the data, but instead let the data organize itself.2

Numeric data is mostly made up of numbers but might use additional symbols to identify units.

Categorical data is made up of words, symbols, phrases, and (confusingly) sometimes numbers, like ZIP codes.

For the sake of example, let's say your cutoff was at 5 missed shots. If the intern had only missed 4 in a row, which has probability 50%4 = 6.25%, or 1-in-16, you could have given him the benefit of the doubt. But once he missed 5 shots, there was too much evidence to the contrary he was a good shooter. That cutoff, 5 missed shots in a row,

“A problem well stated is a problem half solved.”